Encoding of Rational Numbers and Their Homomorphic Computations for FHE-Based Applications

2018 ◽  
Vol 29 (06) ◽  
pp. 1023-1044
Author(s):  
Heewon Chung ◽  
Myungsun Kim
Keyword(s):  
Continued Fractions ◽  
Homomorphic Encryption ◽  
Rational Numbers ◽  
Sensitive Information ◽  
Sensitive Data ◽  
Security Systems ◽  
Complex Arithmetic ◽  
Numeric Data ◽  
High Degree

This work addresses a basic problem of security systems that operate on very sensitive information. Specifically, we are interested in the problem of privately handling numeric data represented by rational numbers (e.g., medical records). Fully homomorphic encryption (FHE) is one of the natural and powerful tools for ensuring privacy of sensitive data, while allowing complicated computations on the data. However, because the native plaintext domain of known FHE schemes is restricted to a set of quite small integers, it is not easy to obtain efficient algorithms for encrypted rational numbers in terms of space and computation costs. For example, the naïve decimal representation considerably restricts the choice of parameters in employing an FHE scheme, particularly the plaintext size. Our basic strategy is to alleviate this inefficiency by using a different representation of rational numbers instead of naïve expressions. In this work we express rational numbers as continued fractions. Because continued fractions enable us to represent rational numbers as a sequence of integers, we can use a plaintext space with a small size while preserving the same quality of precision. However, this encoding technique requires performing very complex arithmetic operations, such as division and modular reduction. Theoretically, FHE allows the evaluation of any function, including modular reduction at encrypted data, but it requires a Boolean circuit of very high degree to be constructed. Hence, the primary contribution of this work is developing an approach to solve this efficiency problem using homomorphic operations with small degrees.

scholarly journals Exploration of Big Data Security Framework using Machine Learning

2020 ◽  
Vol 9 (5) ◽  
pp. 206-209
Keyword(s):  
Machine Learning ◽  
Big Data ◽  
Sensitive Information ◽  
Learning Tools ◽  
Sensitive Data ◽  
Security Systems ◽  
Digital Conversion ◽  
Critical Data ◽  
Cloud Network ◽  
Big Data Technology

As with prior technological advancements, big data technology is growing at present and we have to identify what are the possible threats to overhead the present security systems. Due to the development of recent technical environment like cloud, network connected smartphones and the omnipresent digital conversion of huge volume of all types of data poses more possible threats to sensitive data. Due to the improved vulnerability big data requires increased responsibility. During the last two years, the amount of data that has been created is about 90% of the whole data created. Strengthening the security of sensitive data from unauthorized discovery is the most challenging process in all kind of data processing. Data Leakage Detection offers a set of methods and techniques that can professionally solve the problem arising in particular critical data. The large amounts of existing data is mostly unstructured. To retrieve meaningful information, we have to develop superior analytical method in big data. At present we have more algorithms for security which are not easy to be implement for huge volume of data. We have to protect the sensitive information as well as details related users with the help of security protocols in big data. The sensitive data of the patient, different types of code patterns and set of attributes to be secured by using machine learning tool. Machine learning tools have a lot of library functions to protect the sensitive information about the clients. We recommend the Secure Pattern-Based Data Sensitivity Framework (PBDSF), to protect such sensitive information from big data using Machine Learning. In the proposed framework, HDFS is implemented to analysis the big data, to classify most important information and converting the sensitive data in a secure manner.

Агрономическая эффективность промышленной (голландской) технологии возделывания картофеля

2018 ◽  
Author(s):  
C. Coy ◽  
A.V. Shuravilin ◽  
O.A. Zakharova
Keyword(s):  
Heavy Metals ◽  
Regression Analysis ◽  
Positive Response ◽  
Growth Yield ◽  
Correlation And Regression Analysis ◽  
Yield And Quality ◽  
Quality Of Products ◽  
High Degree ◽  
The Impact

Приведены результаты исследований по изучению влияния промышленной технологии возделывания картофеля на развитие, урожайность и качество продукции. Выявлена положительная реакция растений на подкормку K2SO4 в период посадки. Корреляционно-регрессионный анализ урожайности и качества клубней выявил высокую степень достоверности результатов опыта. Содержание нитратов и тяжелых металлов в клубнях было ниже допустимых величин.The results of studies on the impact of industrial technology of potato cultivation on growth, yield and quality of products. There was a positive response of plants to fertilizer K2SO4 in the period of planting. Correlation and regression analysis of yield and quality of tubers revealed a high degree of reliability of the results of experience. The contents of nitrates and heavy metals in tubers was below the permissible values.

Current Progress in Biomedical Applications of Chitosan-Carbon Nanotube Nanocomposites: A Review

2020 ◽  
Vol 20 (16) ◽  
pp. 1619-1632
Author(s):  
Katarzyna Pieklarz ◽  
Michał Tylman ◽  
Zofia Modrzejewska
Keyword(s):  
Carbon Nanotube ◽  
Medical Science ◽  
Antimicrobial Properties ◽  
Polymer Materials ◽  
Biological Macromolecules ◽  
Medical Sciences ◽  
New Generation ◽  
High Degree ◽  
Dressing Materials

The currently observed development of medical science results from the constant search for innovative solutions to improve the health and quality of life of patients. Particular attention is focused on the design of a new generation of materials with a high degree of biocompatibility and tolerance towards the immune system. In addition, apart from biotolerance, it is important to ensure appropriate mechanical and technological properties of materials intended for intra-body applications. Knowledge of the above parameters becomes the basis for considerations related to the possibilities of choosing the appropriate polymer materials. The researchers' interest, as evidenced by the number of available publications, is attracted by nanobiocomposites based on chitosan and carbon nanotubes, which, due to their properties, enable integration with the tissues of the human body. Nanosystems can be used in many areas of medicine. They constitute an excellent base for use as dressing materials, as they exhibit antimicrobial properties. In addition, they can be carriers of drugs and biological macromolecules and can be used in gene therapy, tissue engineering, and construction of biosensors. For this reason, potential application areas of chitosan-carbon nanotube nanocomposites in medical sciences are presented in this publication, considering the characteristics of the system components.

scholarly journals The Neural Basis of Vivid Memory Is Patterned on Perception

2012 ◽  
Vol 24 (9) ◽  
pp. 1867-1883 ◽  
Author(s):  
Bradley R. Buchsbaum ◽  
Sabrina Lemire-Rodger ◽  
Candice Fang ◽  
Hervé Abdi
Keyword(s):  
Brain Activity ◽  
Sensory Perception ◽  
Direct Perception ◽  
Neural Basis ◽  
Activation Patterns ◽  
Short Video ◽  
Initial Perception ◽  
Experiential Quality ◽  
High Degree

When we have a rich and vivid memory for a past experience, it often feels like we are transported back in time to witness once again this event. Indeed, a perfect memory would exactly mimic the experiential quality of direct sensory perception. We used fMRI and multivoxel pattern analysis to map and quantify the similarity between patterns of activation evoked by direct perception of a diverse set of short video clips and the vivid remembering, with closed eyes, of these clips. We found that the patterns of distributed brain activation during vivid memory mimicked the patterns evoked during sensory perception. Using whole-brain patterns of activation evoked by perception of the videos, we were able to accurately classify brain patterns that were elicited when participants tried to vividly recall those same videos. A discriminant analysis of the activation patterns associated with each video revealed a high degree (explaining over 80% of the variance) of shared representational similarity between perception and memory. These results show that complex, multifeatured memory involves a partial reinstatement of the whole pattern of brain activity that is evoked during initial perception of the stimulus.

scholarly journals Measuring spatial price differentials at the basic heading level: a comparison of stochastic index number methods

2021 ◽  
Author(s):  
Sebastian Weinand
Keyword(s):  
Price Level ◽  
Consumer Price Index ◽  
Index Number ◽  
Price Data ◽  
Estimation Efficiency ◽  
Spatial Price ◽  
Price Comparisons ◽  
High Degree ◽  
The Impact

AbstractSpatial price comparisons rely to a high degree on the quality of the underlying price data that are collected within or across countries. Below the basic heading level, these price data often exhibit large gaps. Therefore, stochastic index number methods like the Country–Product–Dummy (CPD) method and the Gini–Eltetö–Köves–Szulc (GEKS) method are utilised for the aggregation of the price data into higher-level indices. Although the two index number methods produce differing price level estimates when prices are missing, the present paper demonstrates that both can be derived from exactly the same stochastic model. For a specific case of missing prices, it is shown that the formula underlying these price level estimates differs between the two methods only in weighting. The impact of missing prices on the efficiency of the price level estimates is analysed in two simulation studies. It can be shown that the CPD method slightly outperforms the GEKS method. Using micro data of Germany’s Consumer Price Index, it can be observed that more narrowly defined products improve estimation efficiency.

CHARACTERIZATION AND ELECTRICAL PROPERTIES OF ALIGNED CARBON NANOTUBES WITH HIGH ASPECT RATIO

2011 ◽  
Vol 10 (01n02) ◽  
pp. 23-28
Author(s):  
RAVI BHATIA ◽  
V. PRASAD ◽  
M. REGHU
Keyword(s):  
Electron Microscopy ◽  
Carbon Nanotubes ◽  
Aspect Ratio ◽  
Multiwall Carbon Nanotubes ◽  
High Quality ◽  
Transmission Electron Microscopy Analysis ◽  
Transmission Electron ◽  
Electron Microscopy Analysis ◽  
High Degree

High-quality multiwall carbon nanotubes (MWNTs) were produced by a simple one-step technique. The production of MWNTs was based on thermal decomposition of the mixture of a liquid phase organic compound and ferrocene. High degree of alignment was noticed by scanning electron microscopy. The aspect ratio of as-synthesized MWNTs was quite high (more than 4500). Transmission electron microscopy analysis showed the presence of the catalytic iron nanorods at various lengths of MWNTs. Raman spectroscopy was used to know the quality of MWNTs. The ratio of intensity of the G-peak to the D-peak was very high which revealed high quality of MWNTs. Magnetotransport studies were carried out at low temperature and a negative MR was noticed.

scholarly journals Security and privacy concerned association rule mining technique for the accurate frequent pattern identification

2017 ◽  
Vol 7 (1.1) ◽  
pp. 19
Author(s):  
T. Nusrat Jabeen ◽  
M. Chidambaram ◽  
G. Suseendran
Keyword(s):  
Association Rule ◽  
Association Rule Mining ◽  
Frequent Pattern ◽  
Security And Privacy ◽  
Sensitive Information ◽  
Simulation Environment ◽  
Rule Mining ◽  
Mining Technique ◽  
Diffie Hellman

Security and privacy has emerged to be a serious concern in which the business professional don’t desire to share their classified transaction data. In the earlier work, secured sharing of transaction databases are carried out. The performance of those methods is enhanced further by bringing in Security and Privacy aware Large Database Association Rule Mining (SPLD-ARM) framework. Now the Improved Secured Association Rule Mining (ISARM) is introduced for the horizontal and vertical segmentation of huge database. Then k-Anonymization methods referred to as suppression and generalization based Anonymization method is employed for privacy guarantee. At last, Diffie-Hellman encryption algorithm is presented in order to safeguard the sensitive information and for the storage service provider to work on encrypted information. The Diffie-Hellman algorithm is utilized for increasing the quality of the system on the overall by the generation of the secured keys and thus the actual data is protected more efficiently. Realization of the newly introduced technique is conducted in the java simulation environment that reveals that the newly introduced technique accomplishes privacy in addition to security.

Computing Blindfolded on Data Homomorphically Encrypted under Multiple Keys: A Survey

2022 ◽  
Vol 54 (9) ◽  
pp. 1-37
Author(s):  
Asma Aloufi ◽  
Peizhao Hu ◽  
Yongsoo Song ◽  
Kristin Lauter
Keyword(s):  
Homomorphic Encryption ◽  
Lessons Learned ◽  
Secret Key ◽  
Sensitive Data ◽  
Encrypted Data ◽  
Outsourced Computation ◽  
Secret Keys ◽  
Comprehensive Survey ◽  
Multiple Keys ◽  
Cryptographic Techniques

With capability of performing computations on encrypted data without needing the secret key, homomorphic encryption (HE) is a promising cryptographic technique that makes outsourced computations secure and privacy-preserving. A decade after Gentry’s breakthrough discovery of how we might support arbitrary computations on encrypted data, many studies followed and improved various aspects of HE, such as faster bootstrapping and ciphertext packing. However, the topic of how to support secure computations on ciphertexts encrypted under multiple keys does not receive enough attention. This capability is crucial in many application scenarios where data owners want to engage in joint computations and are preferred to protect their sensitive data under their own secret keys. Enabling this capability is a non-trivial task. In this article, we present a comprehensive survey of the state-of-the-art multi-key techniques and schemes that target different systems and threat models. In particular, we review recent constructions based on Threshold Homomorphic Encryption (ThHE) and Multi-Key Homomorphic Encryption (MKHE). We analyze these cryptographic techniques and schemes based on a new secure outsourced computation model and examine their complexities. We share lessons learned and draw observations for designing better schemes with reduced overheads.

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